{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) MONAI Consortium  \n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");  \n",
    "you may not use this file except in compliance with the License.  \n",
    "You may obtain a copy of the License at  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;http://www.apache.org/licenses/LICENSE-2.0  \n",
    "Unless required by applicable law or agreed to in writing, software  \n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,  \n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  \n",
    "See the License for the specific language governing permissions and  \n",
    "limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generative Adversarial Networks with MedNIST Dataset\n",
    "\n",
    "This notebook illustrates the use of MONAI for training a network to generate images from a random input tensor. A simple GAN is employed to do with a separate Generator and Discriminator networks.\n",
    "\n",
    "This will go through the steps of:\n",
    "* Loading the data from a remote source\n",
    "* Constructing a dataset from this data and transforms\n",
    "* Defining the networks\n",
    "* Training and evaluation\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/modules/mednist_GAN_tutorial.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -c \"import monai\" || pip install -q monai-weekly\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from monai.utils import progress_bar, set_determinism\n",
    "from monai.transforms import (\n",
    "    EnsureChannelFirst,\n",
    "    Compose,\n",
    "    RandFlip,\n",
    "    RandRotate,\n",
    "    RandZoom,\n",
    "    ScaleIntensity,\n",
    "    EnsureType,\n",
    "    Transform,\n",
    ")\n",
    "from monai.networks.nets import Discriminator, Generator\n",
    "from monai.networks import normal_init\n",
    "from monai.data import CacheDataset\n",
    "from monai.config import print_config\n",
    "from monai.apps import download_and_extract\n",
    "import numpy as np\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import tempfile\n",
    "\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set deterministic training for reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(seed=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define training variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "disc_train_interval = 1\n",
    "disc_train_steps = 5\n",
    "batch_size = 300\n",
    "latent_size = 64\n",
    "max_epochs = 50\n",
    "real_label = 1\n",
    "gen_label = 0\n",
    "learning_rate = 2e-4\n",
    "betas = (0.5, 0.999)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup data directory\n",
    "\n",
    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
    "This allows you to save results and reuse downloads.  \n",
    "If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/workspace/data/medical\n"
     ]
    }
   ],
   "source": [
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "if directory is not None:\n",
    "    os.makedirs(directory, exist_ok=True)\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "print(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download dataset\n",
    "\n",
    "The MedNIST dataset was gathered from several sets from [TCIA](https://wiki.cancerimagingarchive.net/display/Public/Data+Usage+Policies+and+Restrictions),\n",
    "[the RSNA Bone Age Challenge](https://www.rsna.org/education/ai-resources-and-training/ai-image-challenge/rsna-pediatric-bone-age-challenge-2017),\n",
    "and [the NIH Chest X-ray dataset](https://cloud.google.com/healthcare/docs/resources/public-datasets/nih-chest).\n",
    "\n",
    "The dataset is kindly made available by [Dr. Bradley J. Erickson M.D., Ph.D.](https://www.mayo.edu/research/labs/radiology-informatics/overview) (Department of Radiology, Mayo Clinic)\n",
    "under the Creative Commons [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/).\n",
    "\n",
    "\n",
    "If you use the MedNIST dataset, please acknowledge the source."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method for loading the data from the remote source differs here to demonstrate how to download and read from a tar file without using the filesystem, and because we only want the images of hand X-rays. This isn't a classification example so the category data isn't needed, so we'll download the tarball, open it using the standard library, and recall all of the file names for hands:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "resource = \"https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/MedNIST.tar.gz\"\n",
    "md5 = \"0bc7306e7427e00ad1c5526a6677552d\"\n",
    "\n",
    "compressed_file = os.path.join(root_dir, \"MedNIST.tar.gz\")\n",
    "data_dir = os.path.join(root_dir, \"MedNIST\")\n",
    "if not os.path.exists(data_dir):\n",
    "    download_and_extract(resource, compressed_file, root_dir, md5)\n",
    "\n",
    "hands = [os.path.join(data_dir, \"Hand\", x) for x in os.listdir(os.path.join(data_dir, \"Hand\"))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load the actual image data from the tarfile, we define a transform type to do this using Matplotlib. This is used with other transforms for preparing the data followed by randomized augmentation transforms. The `CacheDataset` class is used here to cache all of the prepared images from the tarball, so we will have in memory all of the prepared images ready to be augmented with randomized rotation, flip, and zoom operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:05<00:00, 1691.00it/s]\n"
     ]
    }
   ],
   "source": [
    "class LoadTarJpeg(Transform):\n",
    "    def __call__(self, data):\n",
    "        return plt.imread(data)\n",
    "\n",
    "\n",
    "train_transforms = Compose(\n",
    "    [\n",
    "        LoadTarJpeg(),\n",
    "        EnsureChannelFirst(channel_dim=\"no_channel\"),\n",
    "        ScaleIntensity(),\n",
    "        RandRotate(range_x=np.pi / 12, prob=0.5, keep_size=True),\n",
    "        RandFlip(spatial_axis=0, prob=0.5),\n",
    "        RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5),\n",
    "        EnsureType(),\n",
    "    ]\n",
    ")\n",
    "\n",
    "train_ds = CacheDataset(hands, train_transforms)\n",
    "train_loader = torch.utils.data.DataLoader(train_ds, batch_size=batch_size, shuffle=True, num_workers=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now define out generator and discriminator networks. The parameters are carefully chosen to suit the image size of `(1, 64, 64)` as loaded from the tar file. Input images to the discriminator are downsampled four times to produce very small images which are flattened and passed as input to a fully-connected layer. The input latent vectors to the generator are passed through a fully-connected layer to produce an output of shape `(64, 8, 8)`, this is then upsampled three times to produce a final output which is the same shape as the real images. The networks are initialized with a normalization scheme to improve results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\")\n",
    "disc_net = Discriminator(\n",
    "    in_shape=(1, 64, 64),\n",
    "    channels=(8, 16, 32, 64, 1),\n",
    "    strides=(2, 2, 2, 2, 1),\n",
    "    num_res_units=1,\n",
    "    kernel_size=5,\n",
    ").to(device)\n",
    "\n",
    "\n",
    "gen_net = Generator(\n",
    "    latent_shape=latent_size,\n",
    "    start_shape=(64, 8, 8),\n",
    "    channels=[32, 16, 8, 1],\n",
    "    strides=[2, 2, 2, 1],\n",
    ")\n",
    "\n",
    "# initialize both networks\n",
    "disc_net.apply(normal_init)\n",
    "gen_net.apply(normal_init)\n",
    "\n",
    "# input images are scaled to [0,1] so enforce the same of generated outputs\n",
    "gen_net.conv.add_module(\"activation\", torch.nn.Sigmoid())\n",
    "gen_net = gen_net.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we define the loss functions to use with helper functions to wrap the loss calculation process for the generator and the discriminator. We also define our optimizers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "disc_loss = torch.nn.BCELoss()\n",
    "gen_loss = torch.nn.BCELoss()\n",
    "\n",
    "disc_opt = torch.optim.Adam(disc_net.parameters(), learning_rate, betas=betas)\n",
    "gen_opt = torch.optim.Adam(gen_net.parameters(), learning_rate, betas=betas)\n",
    "\n",
    "\n",
    "def discriminator_loss(gen_images, real_images):\n",
    "    \"\"\"\n",
    "    The discriminator loss if calculated by comparing its\n",
    "    prediction for real and generated images.\n",
    "\n",
    "    \"\"\"\n",
    "    real = real_images.new_full((real_images.shape[0], 1), real_label)\n",
    "    gen = gen_images.new_full((gen_images.shape[0], 1), gen_label)\n",
    "\n",
    "    realloss = disc_loss(disc_net(real_images), real)\n",
    "    genloss = disc_loss(disc_net(gen_images.detach()), gen)\n",
    "\n",
    "    return (realloss + genloss) / 2\n",
    "\n",
    "\n",
    "def generator_loss(input):\n",
    "    \"\"\"\n",
    "    The generator loss is calculated by determining how well\n",
    "    the discriminator was fooled by the generated images.\n",
    "\n",
    "    \"\"\"\n",
    "    output = disc_net(input)\n",
    "    cats = output.new_full(output.shape, real_label)\n",
    "    return gen_loss(output, cats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now train by iterating over the dataset for a number of epochs. At certain after the generator training stage for each batch, the discriminator is trained for a number of steps on the same real and generated images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33/34 epoch 50, avg loss: 0.0563 [============================= ]\r"
     ]
    }
   ],
   "source": [
    "epoch_loss_values = [(0, 0)]\n",
    "gen_step_loss = []\n",
    "disc_step_loss = []\n",
    "step = 0\n",
    "\n",
    "for epoch in range(max_epochs):\n",
    "    gen_net.train()\n",
    "    disc_net.train()\n",
    "    epoch_loss = 0\n",
    "\n",
    "    for i, batch_data in enumerate(train_loader):\n",
    "        progress_bar(\n",
    "            i,\n",
    "            len(train_loader),\n",
    "            f\"epoch {epoch + 1}, avg loss: {epoch_loss_values[-1][1]:.4f}\",\n",
    "        )\n",
    "        real_images = batch_data.to(device)\n",
    "        latent = torch.randn(real_images.shape[0], latent_size).to(device)\n",
    "\n",
    "        gen_opt.zero_grad()\n",
    "        gen_images = gen_net(latent)\n",
    "        loss = generator_loss(gen_images)\n",
    "        loss.backward()\n",
    "        gen_opt.step()\n",
    "        epoch_loss += loss.item()\n",
    "\n",
    "        gen_step_loss.append((step, loss.item()))\n",
    "\n",
    "        if step % disc_train_interval == 0:\n",
    "            disc_total_loss = 0\n",
    "\n",
    "            for _ in range(disc_train_steps):\n",
    "                disc_opt.zero_grad()\n",
    "                dloss = discriminator_loss(gen_images, real_images)\n",
    "                dloss.backward()\n",
    "                disc_opt.step()\n",
    "                disc_total_loss += dloss.item()\n",
    "\n",
    "            disc_step_loss.append((step, disc_total_loss / disc_train_steps))\n",
    "\n",
    "        step += 1\n",
    "\n",
    "    epoch_loss /= step\n",
    "    epoch_loss_values.append((step, epoch_loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The separate loss values for the generator and discriminator can be graphed together. These should reach an equilibrium as the generator's ability to fool the discriminator balances with that networks ability to discriminate accurately between real and fake images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7efe39ab3490>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.semilogy(*zip(*gen_step_loss), label=\"Generator Loss\")\n",
    "plt.semilogy(*zip(*disc_step_loss), label=\"Discriminator Loss\")\n",
    "plt.grid(True, \"both\", \"both\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we show a few randomly generated images. Hopefully most images will have four fingers and a thumb as expected (assuming polydactyl examples were not present in large numbers in the dataset). This demonstrative notebook doesn't train the networks for long, training beyond the default 50 epochs should improve results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_size = 10\n",
    "test_latent = torch.randn(test_size, latent_size).to(device)\n",
    "\n",
    "test_images = gen_net(test_latent)\n",
    "\n",
    "fig, axs = plt.subplots(1, test_size, figsize=(20, 4))\n",
    "\n",
    "for i, ax in enumerate(axs):\n",
    "    ax.axis(\"off\")\n",
    "    ax.imshow(test_images[i, 0].cpu().data.numpy(), cmap=\"gray\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
